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摘要
由于隧道属于密闭空间且通风条件复杂，隧道火灾构成了严重的能源安全风险。一个典型场景是列车因受电弓故障引发车顶火灾，不仅释放大量热能，还可能导致车载电能与燃料能源的浪费。本研究主要关注纵向通风与横向排气相结合的耦合排烟系统在列车火灾中对能量释放和流场演变的影响，同时探索基于人工智能的列车火灾能量释放预测方法。与传统隧道地面火灾相比，区间隧道列车顶板火灾的燃烧速率呈非单调变化，其流场演变特征也显示出复杂的能量传递模式，对隧道能源管理与安全控制提出了新的要求。同时为了揭示隧道火灾全周期随时间变化的预测过程，克服传统火灾物理模型的时间限制，本文提出了一种基于深度学习的隧道火灾热释放率（HRR）和天花板热通量分布的全周期实时预测框架。该框架集成了ResNet-18和ViT Small用于火焰图像特征提取，同时嵌入物理先验信息作为约束，在隧道纵向通风和横向提取的耦合条件下，实现了对HRR和隧道天花板下热通量分布的快速准确的全周期预测。
Abstract
Tunnel fires pose significant energy safety risks due to the narrow-restricted spaces and complex ventilation conditions. A typical scenario is a train roof fire triggered by a pantograph malfunction, which not only releases substantial thermal energy but also results in the wastage of electrical and fuel energy. This study focuses on the impact of the coupled lateral smoke exhaust system, which combines longitudinal ventilation and lateral exhaust, on fire heat release rate and flow field evolution during train fires, while also exploring an AI-based approach for predicting train fire dynamics. Compared with traditional ground fire in tunnels, the burning rate of train roof fires in interval tunnels shows non-monotonic changes, the evolution characteristics of the flow field also differ significantly. To reveal the prediction process of the full-period changes of tunnel fires over time and overcome the temporal limitations of traditional fire physical models, this paper proposes a full-period, real-time prediction framework for heat release rate (HRR) and ceiling heat flux profile of tunnel fires using deep learning. The framework integrates ResNet-18 and ViT-Small for flame image feature extraction, with physical prior information incorporated as auxiliary input features to enhance the model’s predictive performance and physical interpretability. This enables rapid and accurate full-period prediction of the HRR and the heat flux profile beneath the tunnel ceiling under the coupled conditions of longitudinal ventilation and lateral extraction in a tunnel.
| 关键词 | 隧道火灾、深度学习、质量损失速率、热释放速率、顶棚热流、侧向排烟 |
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| Keywords | Tunnel fire; Deep learning; Mass burning rate; Heat release rate; Ceiling heat flux; Lateral smoke extraction |
